Overview

Dataset statistics

Number of variables14
Number of observations52560
Missing cells1065
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 5 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
blade_angle is highly overall correlated with Rear bearing temperature (°C)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Tower Acceleration y (mm/ss)High correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
# Date and time has unique valuesUnique
blade_angle has 25130 (47.8%) zerosZeros
Rotor speed (RPM) has 1256 (2.4%) zerosZeros

Reproduction

Analysis started2023-07-08 11:55:15.791592
Analysis finished2023-07-08 11:55:32.818602
Duration17.03 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

# Date and time
Date

Distinct52560
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size410.8 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 23:50:00
2023-07-08T17:25:32.867005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:32.958985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52303
Distinct (%)99.7%
Missing81
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean565.11482
Minimum-21.17028
Maximum2077.4192
Zeros1
Zeros (%)< 0.1%
Negative5049
Negative (%)9.6%
Memory size410.8 KiB
2023-07-08T17:25:33.059852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-21.17028
5-th percentile-1.4261051
Q1111.60507
median353.23182
Q3835.95825
95-th percentile1936.8357
Maximum2077.4192
Range2098.5895
Interquartile range (IQR)724.35318

Descriptive statistics

Standard deviation584.03001
Coefficient of variation (CV)1.0334714
Kurtosis0.32828794
Mean565.11482
Median Absolute Deviation (MAD)296.19382
Skewness1.168231
Sum29656661
Variance341091.05
MonotonicityNot monotonic
2023-07-08T17:25:33.155150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.057397026 3
 
< 0.1%
-1.041392025 3
 
< 0.1%
-0.7324955225 3
 
< 0.1%
-1.315611005 3
 
< 0.1%
-1.013116533 3
 
< 0.1%
-1.208911031 3
 
< 0.1%
-1.050995025 3
 
< 0.1%
-0.7314285219 3
 
< 0.1%
-1.308675539 3
 
< 0.1%
-0.72822752 2
 
< 0.1%
Other values (52293) 52450
99.8%
(Missing) 81
 
0.2%
ValueCountFrequency (%)
-21.17028046 1
< 0.1%
-18.07535558 1
< 0.1%
-18.04303694 1
< 0.1%
-17.2803421 1
< 0.1%
-16.4377656 1
< 0.1%
-15.39090838 1
< 0.1%
-14.92042787 1
< 0.1%
-14.66160796 1
< 0.1%
-14.62062653 1
< 0.1%
-14.3967606 1
< 0.1%
ValueCountFrequency (%)
2077.419189 1
< 0.1%
2077.003906 1
< 0.1%
2072.185034 1
< 0.1%
2071.355951 1
< 0.1%
2071.223621 1
< 0.1%
2070.820294 1
< 0.1%
2070.04184 1
< 0.1%
2069.497437 1
< 0.1%
2069.127881 1
< 0.1%
2068.510083 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52475
Distinct (%)> 99.9%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean202.5292
Minimum0.010384002
Maximum359.98202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:33.248501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.010384002
5-th percentile31.526752
Q1147.49247
median216.81564
Q3266.44482
95-th percentile331.77653
Maximum359.98202
Range359.97164
Interquartile range (IQR)118.95235

Descriptive statistics

Standard deviation89.092887
Coefficient of variation (CV)0.43990145
Kurtosis-0.51694993
Mean202.5292
Median Absolute Deviation (MAD)56.598346
Skewness-0.51686354
Sum10628327
Variance7937.5425
MonotonicityNot monotonic
2023-07-08T17:25:33.343499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
314.3288269 2
 
< 0.1%
279.4419556 2
 
< 0.1%
272.8258057 2
 
< 0.1%
271.767229 1
 
< 0.1%
263.9863547 1
 
< 0.1%
269.8546754 1
 
< 0.1%
267.8074282 1
 
< 0.1%
274.7846123 1
 
< 0.1%
273.1868789 1
 
< 0.1%
277.9195508 1
 
< 0.1%
Other values (52465) 52465
99.8%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
0.01038400192 1
< 0.1%
0.01337546593 1
< 0.1%
0.01917998115 1
< 0.1%
0.02074374608 1
< 0.1%
0.03072651613 1
< 0.1%
0.06003311718 1
< 0.1%
0.06741348579 1
< 0.1%
0.06865403115 1
< 0.1%
0.07069122663 1
< 0.1%
0.07235729889 1
< 0.1%
ValueCountFrequency (%)
359.9820229 1
< 0.1%
359.9746763 1
< 0.1%
359.9495383 1
< 0.1%
359.9353625 1
< 0.1%
359.9195571 1
< 0.1%
359.91347 1
< 0.1%
359.9029012 1
< 0.1%
359.8591405 1
< 0.1%
359.8356074 1
< 0.1%
359.8222849 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct12268
Distinct (%)23.4%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean203.34237
Minimum0.070337872
Maximum359.95719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:33.445896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.070337872
5-th percentile31.562317
Q1149.00119
median217.83502
Q3268.63519
95-th percentile333.65573
Maximum359.95719
Range359.88685
Interquartile range (IQR)119.634

Descriptive statistics

Standard deviation89.449123
Coefficient of variation (CV)0.43989417
Kurtosis-0.51846344
Mean203.34237
Median Absolute Deviation (MAD)56.761164
Skewness-0.51275253
Sum10671001
Variance8001.1456
MonotonicityNot monotonic
2023-07-08T17:25:33.542396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199.4891663 286
 
0.5%
200.586731 232
 
0.4%
254.3674011 227
 
0.4%
208.2696838 202
 
0.4%
197.2940369 198
 
0.4%
221.4404602 187
 
0.4%
224.7331543 185
 
0.4%
228.0258484 184
 
0.4%
198.3905029 179
 
0.3%
253.2698364 179
 
0.3%
Other values (12258) 50419
95.9%
ValueCountFrequency (%)
0.07033787223 1
< 0.1%
0.224748485 1
< 0.1%
0.2878379899 1
< 0.1%
0.3698919195 1
< 0.1%
0.530439228 1
< 0.1%
0.5319634836 1
< 0.1%
0.5530811531 1
< 0.1%
0.6061760187 1
< 0.1%
0.6127598338 1
< 0.1%
0.6447896332 1
< 0.1%
ValueCountFrequency (%)
359.957191 1
 
< 0.1%
359.9244288 1
 
< 0.1%
359.8156738 1
 
< 0.1%
359.7336121 47
0.1%
359.7330627 66
0.1%
359.7330627 6
 
< 0.1%
359.7325134 40
0.1%
359.7312003 1
 
< 0.1%
359.726593 1
 
< 0.1%
359.6142737 1
 
< 0.1%

blade_angle
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18431
Distinct (%)35.1%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.3148542
Minimum0
Maximum92.510002
Zeros25130
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:33.646185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.024499745
Q30.96499985
95-th percentile44.990002
Maximum92.510002
Range92.510002
Interquartile range (IQR)0.96499985

Descriptive statistics

Standard deviation15.084334
Coefficient of variation (CV)2.8381463
Kurtosis13.100122
Mean5.3148542
Median Absolute Deviation (MAD)0.024499745
Skewness3.5113916
Sum278912.92
Variance227.53713
MonotonicityNot monotonic
2023-07-08T17:25:33.740173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25130
47.8%
44.99000168 2655
 
5.1%
0.02449974513 753
 
1.4%
89.98999786 421
 
0.8%
1.49000001 386
 
0.7%
0.0489995709 286
 
0.5%
44.99333445 259
 
0.5%
0.07349946834 161
 
0.3%
0.0979994285 118
 
0.2%
1.49333334 97
 
0.2%
Other values (18421) 22212
42.3%
ValueCountFrequency (%)
0 25130
47.8%
0.0001666666622 20
 
< 0.1%
0.0003333333104 1
 
< 0.1%
0.0003333333104 1
 
< 0.1%
0.0003333333201 5
 
< 0.1%
0.0003333333244 15
 
< 0.1%
0.000342105254 1
 
< 0.1%
0.0003508771791 1
 
< 0.1%
0.0003508771836 3
 
< 0.1%
0.0003703703793 1
 
< 0.1%
ValueCountFrequency (%)
92.51000214 1
 
< 0.1%
92.49333191 35
0.1%
92.49316521 1
 
< 0.1%
92.4928318 2
 
< 0.1%
92.4923317 1
 
< 0.1%
92.49199829 1
 
< 0.1%
92.49049797 1
 
< 0.1%
92.48999786 9
 
< 0.1%
92.45400289 1
 
< 0.1%
92.40716602 1
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

Distinct38731
Distinct (%)73.8%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean63.260901
Minimum13.1475
Maximum74.4975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:33.835439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13.1475
5-th percentile43.110447
Q161.3375
median66.192303
Q368.705
95-th percentile70.9575
Maximum74.4975
Range61.35
Interquartile range (IQR)7.3674998

Descriptive statistics

Standard deviation8.8228379
Coefficient of variation (CV)0.13946747
Kurtosis5.4617791
Mean63.260901
Median Absolute Deviation (MAD)3.0851974
Skewness-2.197089
Sum3319805.6
Variance77.842469
MonotonicityNot monotonic
2023-07-08T17:25:33.928723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.76249962 9
 
< 0.1%
68.31499977 9
 
< 0.1%
67.75 8
 
< 0.1%
69.22499962 8
 
< 0.1%
67.90749969 8
 
< 0.1%
27.60000038 8
 
< 0.1%
26.79999924 8
 
< 0.1%
66 8
 
< 0.1%
67.5 8
 
< 0.1%
67.68000031 8
 
< 0.1%
Other values (38721) 52396
99.7%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
13.14750004 1
< 0.1%
13.19999981 1
< 0.1%
13.22500038 1
< 0.1%
13.3550005 1
< 0.1%
13.48750019 1
< 0.1%
13.63250065 1
< 0.1%
13.67249966 1
< 0.1%
13.68500042 1
< 0.1%
13.82499981 1
< 0.1%
13.82999992 1
< 0.1%
ValueCountFrequency (%)
74.49749985 1
< 0.1%
73.58249969 1
< 0.1%
73.39999924 1
< 0.1%
73.32631563 1
< 0.1%
73.27250023 1
< 0.1%
73.09249992 1
< 0.1%
73.04000015 1
< 0.1%
72.97249947 1
< 0.1%
72.96499977 1
< 0.1%
72.95999985 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51154
Distinct (%)97.5%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean10.273215
Minimum0
Maximum15.332752
Zeros1256
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:34.028539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.59892997
Q18.2437831
median10.389887
Q313.440631
95-th percentile15.145931
Maximum15.332752
Range15.332752
Interquartile range (IQR)5.1968479

Descriptive statistics

Standard deviation3.9082671
Coefficient of variation (CV)0.38043271
Kurtosis0.80924692
Mean10.273215
Median Absolute Deviation (MAD)2.2426765
Skewness-0.97410786
Sum539117.79
Variance15.274552
MonotonicityNot monotonic
2023-07-08T17:25:34.125324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1256
 
2.4%
0.0110000018 8
 
< 0.1%
0.01050000242 6
 
< 0.1%
0.01200000197 4
 
< 0.1%
0.01150000188 4
 
< 0.1%
0.01300000213 3
 
< 0.1%
15.1445322 3
 
< 0.1%
0.02250000369 3
 
< 0.1%
0.01400000229 3
 
< 0.1%
8.143354565 2
 
< 0.1%
Other values (51144) 51186
97.4%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
0 1256
2.4%
0.0001320000301 1
 
< 0.1%
0.00145200023 1
 
< 0.1%
0.01050000242 6
 
< 0.1%
0.0110000018 8
 
< 0.1%
0.01150000188 4
 
< 0.1%
0.01200000197 1
 
< 0.1%
0.01200000197 4
 
< 0.1%
0.01250000205 1
 
< 0.1%
0.01300000213 3
 
< 0.1%
ValueCountFrequency (%)
15.33275159 1
< 0.1%
15.29023947 1
< 0.1%
15.28787662 1
< 0.1%
15.28692462 1
< 0.1%
15.28657092 1
< 0.1%
15.28628129 1
< 0.1%
15.28585942 1
< 0.1%
15.28245986 1
< 0.1%
15.27984783 1
< 0.1%
15.27980232 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52456
Distinct (%)> 99.9%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1218.4675
Minimum-590.99683
Maximum1815.1449
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)< 0.1%
Memory size410.8 KiB
2023-07-08T17:25:34.347409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-590.99683
5-th percentile71.011796
Q1978.77495
median1232.998
Q31592.8642
95-th percentile1794.8215
Maximum1815.1449
Range2406.1417
Interquartile range (IQR)614.08921

Descriptive statistics

Standard deviation462.76527
Coefficient of variation (CV)0.37979286
Kurtosis0.82570573
Mean1218.4675
Median Absolute Deviation (MAD)265.33126
Skewness-0.98082618
Sum63942739
Variance214151.7
MonotonicityNot monotonic
2023-07-08T17:25:34.440187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
987.0085945 2
 
< 0.1%
966.4132347 2
 
< 0.1%
1793.478149 2
 
< 0.1%
968.5808105 2
 
< 0.1%
965.697916 2
 
< 0.1%
966.0752163 2
 
< 0.1%
966.3011742 2
 
< 0.1%
1.162178781 2
 
< 0.1%
1712.39209 2
 
< 0.1%
965.7683086 2
 
< 0.1%
Other values (52446) 52458
99.8%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
-590.9968262 1
< 0.1%
-578.8864136 1
< 0.1%
-117.7441812 1
< 0.1%
-56.21931959 1
< 0.1%
0.4516634345 1
< 0.1%
0.4531117678 1
< 0.1%
0.4553062022 1
< 0.1%
0.4567545354 1
< 0.1%
0.4706453383 1
< 0.1%
0.4727300406 1
< 0.1%
ValueCountFrequency (%)
1815.144854 1
< 0.1%
1814.263905 1
< 0.1%
1812.712315 1
< 0.1%
1811.650211 1
< 0.1%
1811.608767 1
< 0.1%
1811.430702 1
< 0.1%
1810.508598 1
< 0.1%
1810.462561 1
< 0.1%
1810.155716 1
< 0.1%
1809.977806 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

Distinct35001
Distinct (%)66.7%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean10.769821
Minimum-0.91000003
Maximum35.225
Zeros0
Zeros (%)0.0%
Negative226
Negative (%)0.4%
Memory size410.8 KiB
2023-07-08T17:25:34.533358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.91000003
5-th percentile2.7318215
Q16.535
median10.04
Q314.66
95-th percentile20.432875
Maximum35.225
Range36.135
Interquartile range (IQR)8.125

Descriptive statistics

Standard deviation5.5996037
Coefficient of variation (CV)0.51993468
Kurtosis0.037919946
Mean10.769821
Median Absolute Deviation (MAD)3.9799999
Skewness0.51410639
Sum565178.68
Variance31.355561
MonotonicityNot monotonic
2023-07-08T17:25:34.629276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.5 29
 
0.1%
6.099999905 26
 
< 0.1%
8.199999809 25
 
< 0.1%
6.699999809 24
 
< 0.1%
4.900000095 22
 
< 0.1%
7.099999905 21
 
< 0.1%
11 20
 
< 0.1%
6 20
 
< 0.1%
10 19
 
< 0.1%
8.600000381 18
 
< 0.1%
Other values (34991) 52254
99.4%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
-0.9100000262 1
< 0.1%
-0.875 1
< 0.1%
-0.7925000191 1
< 0.1%
-0.7725000381 1
< 0.1%
-0.7250000238 1
< 0.1%
-0.7225000262 1
< 0.1%
-0.6999999881 1
< 0.1%
-0.655000031 1
< 0.1%
-0.6425000429 1
< 0.1%
-0.6175000072 2
< 0.1%
ValueCountFrequency (%)
35.22500019 1
< 0.1%
35.19000053 1
< 0.1%
35.16500015 1
< 0.1%
35.04499989 1
< 0.1%
35.02500057 1
< 0.1%
35.01499996 1
< 0.1%
35.01000061 1
< 0.1%
34.96052712 1
< 0.1%
34.9350008 1
< 0.1%
34.84750137 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

Distinct38939
Distinct (%)74.2%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean64.997972
Minimum14.565001
Maximum79.782501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:34.733130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.565001
5-th percentile42.726927
Q160.905625
median69.353749
Q372.145
95-th percentile73.855264
Maximum79.782501
Range65.217501
Interquartile range (IQR)11.239375

Descriptive statistics

Standard deviation10.197788
Coefficient of variation (CV)0.15689393
Kurtosis2.5087814
Mean64.997972
Median Absolute Deviation (MAD)3.721249
Skewness-1.6395734
Sum3410963.6
Variance103.99487
MonotonicityNot monotonic
2023-07-08T17:25:34.825518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.29999924 30
 
0.1%
72.16999969 13
 
< 0.1%
71.52750015 9
 
< 0.1%
72.92999954 8
 
< 0.1%
71.43499985 8
 
< 0.1%
70.9375 8
 
< 0.1%
72.90249977 8
 
< 0.1%
71.39249992 8
 
< 0.1%
72.20500031 8
 
< 0.1%
72.1625 8
 
< 0.1%
Other values (38929) 52370
99.6%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
14.56500053 1
< 0.1%
14.68499947 1
< 0.1%
14.9024992 1
< 0.1%
15 1
< 0.1%
15.02499962 1
< 0.1%
15.06750011 1
< 0.1%
15.07750034 1
< 0.1%
15.10000038 1
< 0.1%
15.24499989 1
< 0.1%
15.28750038 1
< 0.1%
ValueCountFrequency (%)
79.78250122 1
< 0.1%
79.20999908 1
< 0.1%
79.09750023 1
< 0.1%
78.75250244 1
< 0.1%
78.67999916 1
< 0.1%
78.66500092 1
< 0.1%
78.64687347 1
< 0.1%
78.4375 1
< 0.1%
78.34000015 1
< 0.1%
78.24999886 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

Distinct52472
Distinct (%)> 99.9%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean56.782792
Minimum1.225289
Maximum221.03295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:34.927324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.225289
5-th percentile4.5140782
Q135.739913
median54.4939
Q377.222913
95-th percentile109.78161
Maximum221.03295
Range219.80767
Interquartile range (IQR)41.483

Descriptive statistics

Standard deviation30.685599
Coefficient of variation (CV)0.54040314
Kurtosis-0.015782238
Mean56.782792
Median Absolute Deviation (MAD)20.550673
Skewness0.36502395
Sum2979847.3
Variance941.60598
MonotonicityNot monotonic
2023-07-08T17:25:35.021951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.6712265 2
 
< 0.1%
3.859832048 2
 
< 0.1%
4.275280797 2
 
< 0.1%
83.35513296 2
 
< 0.1%
69.94718933 2
 
< 0.1%
4.700440717 2
 
< 0.1%
51.47847366 1
 
< 0.1%
54.12261777 1
 
< 0.1%
77.62322321 1
 
< 0.1%
74.93535504 1
 
< 0.1%
Other values (52462) 52462
99.8%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
1.225288987 1
< 0.1%
2.425408572 1
< 0.1%
2.450585008 1
< 0.1%
2.474638239 1
< 0.1%
2.519882324 1
< 0.1%
2.567596328 1
< 0.1%
2.638634041 1
< 0.1%
2.649692538 1
< 0.1%
2.65070295 1
< 0.1%
2.683962363 1
< 0.1%
ValueCountFrequency (%)
221.0329548 1
< 0.1%
216.5936703 1
< 0.1%
201.3490196 1
< 0.1%
199.1037621 1
< 0.1%
198.5423664 1
< 0.1%
196.3081978 1
< 0.1%
195.9524221 1
< 0.1%
193.8001208 1
< 0.1%
192.0357487 1
< 0.1%
189.9926926 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct52341
Distinct (%)99.7%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.8625296
Minimum0.18165033
Maximum22.253396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:35.120692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.18165033
5-th percentile2.197895
Q13.9703818
median5.5871337
Q37.3172933
95-th percentile10.665815
Maximum22.253396
Range22.071745
Interquartile range (IQR)3.3469115

Descriptive statistics

Standard deviation2.6451987
Coefficient of variation (CV)0.45120432
Kurtosis1.307752
Mean5.8625296
Median Absolute Deviation (MAD)1.6692421
Skewness0.85764797
Sum307653.83
Variance6.9970759
MonotonicityNot monotonic
2023-07-08T17:25:35.214153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.994730091 2
 
< 0.1%
5.921425843 2
 
< 0.1%
3.080787796 2
 
< 0.1%
9.06921742 2
 
< 0.1%
6.193811941 2
 
< 0.1%
6.270957685 2
 
< 0.1%
4.150581837 2
 
< 0.1%
6.26013577 2
 
< 0.1%
4.474943161 2
 
< 0.1%
5.59706471 2
 
< 0.1%
Other values (52331) 52458
99.8%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
0.1816503339 1
< 0.1%
0.187950328 1
< 0.1%
0.1931628205 1
< 0.1%
0.2200315714 1
< 0.1%
0.233052731 1
< 0.1%
0.2935314234 1
< 0.1%
0.301425159 1
< 0.1%
0.3024564069 1
< 0.1%
0.3202312633 1
< 0.1%
0.3214644211 1
< 0.1%
ValueCountFrequency (%)
22.25339568 1
< 0.1%
20.94563813 1
< 0.1%
20.82512894 1
< 0.1%
20.74251391 1
< 0.1%
20.6275125 1
< 0.1%
20.01875615 1
< 0.1%
19.81174631 1
< 0.1%
19.76928771 1
< 0.1%
19.71419892 1
< 0.1%
19.68084192 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

Distinct52473
Distinct (%)> 99.9%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean27.344907
Minimum2.9097064
Maximum178.47799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:35.309006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.9097064
5-th percentile4.6833338
Q117.241769
median25.358523
Q334.807717
95-th percentile54.353501
Maximum178.47799
Range175.56828
Interquartile range (IQR)17.565948

Descriptive statistics

Standard deviation15.458616
Coefficient of variation (CV)0.56531974
Kurtosis4.4994193
Mean27.344907
Median Absolute Deviation (MAD)8.6790635
Skewness1.409685
Sum1435006
Variance238.9688
MonotonicityNot monotonic
2023-07-08T17:25:35.405821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.54289246 2
 
< 0.1%
16.82113075 2
 
< 0.1%
39.26812363 2
 
< 0.1%
16.09809517 2
 
< 0.1%
27.66831589 2
 
< 0.1%
27.37782102 1
 
< 0.1%
33.01093543 1
 
< 0.1%
26.22505062 1
 
< 0.1%
29.37970366 1
 
< 0.1%
23.06736987 1
 
< 0.1%
Other values (52463) 52463
99.8%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
2.909706351 1
< 0.1%
3.013072826 1
< 0.1%
3.015715164 1
< 0.1%
3.02255606 1
< 0.1%
3.022928235 1
< 0.1%
3.031567398 1
< 0.1%
3.036765319 1
< 0.1%
3.038096359 1
< 0.1%
3.063879135 1
< 0.1%
3.068116504 1
< 0.1%
ValueCountFrequency (%)
178.4779887 1
< 0.1%
171.0480473 1
< 0.1%
169.2796755 1
< 0.1%
163.0561365 1
< 0.1%
151.9242058 1
< 0.1%
143.9993296 1
< 0.1%
143.4720064 1
< 0.1%
142.6056885 1
< 0.1%
140.6470033 1
< 0.1%
139.1597837 1
< 0.1%

Metal particle count counter
Real number (ℝ)

Distinct58
Distinct (%)0.1%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean449.35316
Minimum420
Maximum480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:35.509780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum420
5-th percentile425
Q1435
median447
Q3468
95-th percentile478
Maximum480
Range60
Interquartile range (IQR)33

Descriptive statistics

Standard deviation18.138119
Coefficient of variation (CV)0.040364953
Kurtosis-1.4214048
Mean449.35316
Median Absolute Deviation (MAD)18
Skewness0.16097287
Sum23581155
Variance328.99137
MonotonicityIncreasing
2023-07-08T17:25:35.606703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
435 4149
 
7.9%
448 3934
 
7.5%
438 3638
 
6.9%
474 3609
 
6.9%
469 3332
 
6.3%
428 3107
 
5.9%
465 2990
 
5.7%
468 2578
 
4.9%
478 2278
 
4.3%
427 2047
 
3.9%
Other values (48) 20816
39.6%
ValueCountFrequency (%)
420 415
 
0.8%
422 1040
 
2.0%
423 417
 
0.8%
424 275
 
0.5%
425 1742
3.3%
426 555
 
1.1%
427 2047
3.9%
428 3107
5.9%
429 9
 
< 0.1%
430 608
 
1.2%
ValueCountFrequency (%)
480 818
 
1.6%
479 6
 
< 0.1%
478 2278
4.3%
477 137
 
0.3%
476 351
 
0.7%
475 218
 
0.4%
474 3609
6.9%
473 688
 
1.3%
471 6
 
< 0.1%
470 1
 
< 0.1%

Interactions

2023-07-08T17:25:30.950716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:17.294848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.366004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.506034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.742651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.821563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.938331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.222967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.310297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.466333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.561642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.730190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.803365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.031641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:17.368265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.446871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.586800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.820566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.901160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.022949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.297044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.395344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.544881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.638246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.805857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.887321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.120527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:17.453444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.537502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.677866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.906583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.991822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.115006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.384711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.487790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.632141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.722054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.891983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.978483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.212404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:17.539914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.626720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.767931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.994295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.081267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.209793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.471664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.582255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.721354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.806725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.978138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.070239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.292679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:17.616443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.710191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.851268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.070025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.162135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.292591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.550668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.665313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.799115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.881710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.056657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.152961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.379229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:17.697552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.798130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.937259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.150964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.245262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.382061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.633406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.755457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.883041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.961661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.137890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.242703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.470333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:17.788336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.893324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.133093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.241416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.336548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.474884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.723142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.849887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.973048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.050091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.228477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.335891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.553284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:17.864486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.975993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.212964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.318087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.418761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.560102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.801560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.935104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.053776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.126087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.304566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.420095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.643518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:17.953752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.069075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.305470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.407030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.508650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.653060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.889261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.026188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.141979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.327207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.391440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.512826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.732319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.037808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.158717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.394792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.491021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.597028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.747089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.977060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.118678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.227401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.408734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.477757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.603517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.814511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.116916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.242740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.478181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.570530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.678643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.832391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.055677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.208061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.307046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.484502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.554577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.686105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.896000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.193993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.325022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.560333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.647983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.760650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:23.921141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.135598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.289628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.385677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.560576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.632731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.770051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:31.987041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:18.279815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:19.415117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:20.653005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:21.734327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:22.850768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:24.014542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:25.223581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:26.377977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:27.475244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:28.644741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:29.718502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:30.860139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:25:35.696437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.0440.035-0.3330.7480.9940.993-0.1930.9000.4760.9780.7390.010
Wind direction (°)0.0441.0000.8920.0170.0240.0470.047-0.0400.0370.2240.0200.165-0.079
Nacelle position (°)0.0350.8921.0000.0240.0160.0370.038-0.0430.0300.2100.0090.153-0.086
blade_angle-0.3330.0170.0241.000-0.565-0.338-0.3380.164-0.437-0.107-0.311-0.127-0.054
Rear bearing temperature (°C)0.7480.0240.016-0.5651.0000.7490.7470.0250.8980.3380.7270.5060.062
Rotor speed (RPM)0.9940.0470.037-0.3380.7491.0000.999-0.1870.9010.4780.9710.7380.012
Generator RPM (RPM)0.9930.0470.038-0.3380.7470.9991.000-0.1980.9000.4780.9710.7380.011
Nacelle ambient temperature (°C)-0.193-0.040-0.0430.1640.025-0.187-0.1981.000-0.136-0.108-0.167-0.1370.142
Front bearing temperature (°C)0.9000.0370.030-0.4370.8980.9010.900-0.1361.0000.4120.8770.6460.039
Tower Acceleration X (mm/ss)0.4760.2240.210-0.1070.3380.4780.478-0.1080.4121.0000.4090.824-0.033
Wind speed (m/s)0.9780.0200.009-0.3110.7270.9710.971-0.1670.8770.4091.0000.6900.028
Tower Acceleration y (mm/ss)0.7390.1650.153-0.1270.5060.7380.738-0.1370.6460.8240.6901.000-0.025
Metal particle count counter0.010-0.079-0.086-0.0540.0620.0120.0110.1420.039-0.0330.028-0.0251.000

Missing values

2023-07-08T17:25:32.226737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:25:32.422049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:25:32.650728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02019-01-01 00:00:00270.857422274.425659281.8059690.00000065.2699979.6287401143.1813967.940067.34000451.4784745.05949519.788769420.0
12019-01-01 00:10:00337.457855278.419189281.8059690.00000066.23999810.2422961216.8350837.950068.13749750.2573055.32792721.771482420.0
22019-01-01 00:20:00349.862000282.561188281.8059690.00000067.27000410.3619961230.4906017.970069.26000269.5149315.48043624.772978420.0
32019-01-01 00:30:00349.753662282.268036281.8059690.00000067.26000210.3453711229.3179937.985069.33750287.1128925.36713134.226437420.0
42019-01-01 00:40:0048.554565297.793396281.8059691.05151063.9800008.254738980.7716678.090065.419998112.2602843.12114333.683140420.0
52019-01-01 00:50:0092.065613318.999847305.5047000.51349762.5825008.245139979.9702158.012562.63499887.3682793.78371327.101835420.0
62019-01-01 01:00:00147.590393313.222626313.6353450.19766063.5975008.5352101014.5509647.860063.75250294.7076424.03406527.395365420.0
72019-01-01 01:10:0022.218241311.303131313.6353451.27150761.7675028.191750973.2408457.837560.91500172.2100372.57614820.217831420.0
82019-01-01 01:20:0026.300817300.301636313.6353451.22283560.6074988.176588971.4862677.720058.75750078.8017653.07497820.944176420.0
92019-01-01 01:30:00126.060661281.519531292.4614870.39648861.3849988.4739451006.2184457.705059.97499861.0505494.19633823.664944420.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
525502019-12-31 22:20:00211.06431693.623488107.2938690.00062.2575009.0172251071.3168476.082563.57500144.4548614.85501024.655736480.0
525512019-12-31 22:30:00221.54778395.319689107.2938690.00062.6000009.1323221084.2906076.042564.14750040.0251775.11783719.805580480.0
525522019-12-31 22:40:00164.38228586.325955106.5622010.04962.1150008.5465971015.2370725.795063.66250134.6316704.81124027.127350480.0
525532019-12-31 22:50:00212.81383185.90400876.5622710.00062.4750009.0151021070.9194395.515064.01750131.7846874.55684119.572005480.0
525542019-12-31 23:00:00237.45184091.40829776.5622710.00062.6950019.2987691104.3697245.530064.57250026.6133874.78514124.109300480.0
525552019-12-31 23:10:00215.13676191.14117176.5622710.00062.3650009.0419881074.4783745.525064.29750141.5391984.73535622.409897480.0
525562019-12-31 23:20:00215.47607389.05042476.5622710.00062.2575019.0340601073.5085565.565064.12250033.6004854.80455719.756412480.0
525572019-12-31 23:30:00323.93277392.50669576.5622710.00063.73999910.1136161201.6743565.500065.62500040.0269185.28192918.436137480.0
525582019-12-31 23:40:00367.48513994.12793897.8905280.00065.09750110.5105891248.8998665.562567.34000143.1535575.82889219.659131480.0
525592019-12-31 23:50:00265.015195102.798941104.0011980.00064.2500009.5736401136.4221025.697566.86750283.9026364.99079531.024097480.0